Serverless computing enables you to outsource information, thus freeing teams to focus on front-end work. Organizations choose serverless when they look for faster and easier ways to release updates and patches. DevOps teams often use serverless when they are looking to automate infrastructure tasks. However, each provider offers different capabilities, and each service does require a level of training pre-adoption.
What Is Serverless Computing?
Serverless computing is a method that outsources infrastructure and infrastructure tasks to external providers. Organizations choose serverless computing because it enables development and operations teams to focus on the front-end of products and services.
There are a few traits that distinguish serverless infrastructures from traditional cloud automation models:
- Serverless computing services eliminate user responsibility for the management or operation of infrastructure.
- Serverless computing services enable users to run code on-demand according to requests or event triggers. Infrastructure use scales transparently based on how many requests or events occur.
- Serverless computing is pay-for-use and can be used to ensure that users pay only for actively used resources.
The benefits of serverless in enterprise environments include:
- Better control over production deployments.
- Faster and easier updates or patches.
- Increased productivity and easier release rollouts.
Popular Serverless Computing Services
All three major cloud providers offer serverless computing services. Below you can learn about these services and how each operates.
AWS Lambda
AWS Lambda is a service for serverless computing with other AWS services or via API. It can be connected to applications or run as standalone code. When using Lambda, users pay for active compute time although instances can be reserved to ensure their code runs with menial latency.
Lambda can be triggered by a variety of AWS services, including Kinesis and DynamoDB. It can be monitored through Amazon Cloud Watch. Common use cases for Lambda include data processing, stream processing, machine learning and application backends.
Azure Functions
Azure Functions is Microsoft’s serverless service. You can use it for end-to-end development, including building, testing and deploying code. Functions can be used with a variety of Azure services and integrate with Visual Studio and Visual Studio Code for easy use.
In addition to Functions, Azure offers Logic Apps, a service for creating visual design workflows and orchestrating Functions activities, and Event Grid, a message routing service to integrate Functions with other Azure services.
Google Cloud Functions
Cloud Functions is Google Cloud Platform’s functions as a service (FaaS) (i.e. serverless) offering. It can be used in the cloud or with hybrid deployments and it includes built-in monitoring, logging and debugging features.
Cloud Functions supports a variety of environments including Cloud Run, Kubernetes and other Knative-based environments. Use cases for Cloud Functions include integration with third-party services, application backends, real-time processing, virtual assistants and machine learning.
Why Serverless for DevOps
If you are already using a DevOps approach or are considering adopting one, serverless can be a good option. Many DevOps teams, especially those operating in the cloud, are already working with modular components managed via a pipeline. Moving these modular services to serverless can enable teams to eliminate some of the tasks related to pipeline management and focus on development and deployment.
This capability is especially valuable for teams that are just getting started or are looking to switch to DevOps practices. Serverless systems can enable teams to get started with development and testing right away on a comfortable scale. Then, as productivity increases, the serverless infrastructure can smoothly scale with minimal to no effort on the part of operations.
Another benefit of serverless infrastructures is the ability to easily enable DevOps automation. With serverless, you can deploy your pipeline without hosted solutions. You are only responsible for “glue code.” This infrastructure is flexible and you can easily modify it as needed using infrastructure as code (IaC) solutions and automated events.
Despite these benefits, there is a potential downside to serverless. Your DevOps team does not have the same visibility into or control of the functioning of the infrastructure. While you can integrate monitoring, teams are limited in how much of the infrastructure can be customized or manually affected.
How Serverless Can Take DevOps to the Next Level
There are several ways serverless deployments can be leveraged to improve DevOps productivity. Below are a few examples to consider.
Infrastructure as Code
Provisioning infrastructure is often a significant portion of deployment processes. Many applications use a variety of serverless services as building blocks through all stages of development and release. Making sure that these services are correctly connected and configured is key to a successful deployment.
IaC can help users accomplish these tasks smoothly by leveraging declarative languages and tools such as Terraform. With these solutions, users can version your infrastructure along with their code, keeping tasks in sync and deployments consistent.
Flexible Deployments
With serverless services, infrastructure is immutable. Any changes result in new instances being spun up and activated. This means both old and new versions can run simultaneously.
This immutability can be used to perform flexible deployments that are not possible or are much more difficult with traditional infrastructures; for example, canary or blue-green deployments. With serverless infrastructure, users can switch from one version to another with minimal fear of service interruptions and the ability to easily rollback updates.
Distributed DevOps
Serverless services can enable DevOps teams to work across a variety of environments and locations with minimal impact on others. These services allow the distribution of workloads at scale and can ensure that infrastructure is always available.
The catch is that greater distribution and the ephemeral nature of serverless can make infrastructure much more difficult to monitor effectively. To account for this, teams need to ensure centralized monitoring with real-time alerts. Teams should also periodically audit infrastructure availability and performance to ensure that resources are functioning as expected.
Conclusion
Serverless computing enables DevOps teams to eliminate time and cost on the back end. Each serverless provider offers different services that can help achieve different capabilities. The main capabilities are provided via the use of functions, which enable the automation of infrastructure. However, in most cases, teams do need to learn how to use functions to properly configure customized IaC workflows. Once teams understand how to use the software, they can create flexible deployments and a distributed DevOps pipeline.
